code: def test_smoothL1_random_derivative(backend_default, fargs):
      s1, s2, m, sigma = fargs
      sigma2 = sigma**2
      shape = (s1, s2)
      magnitude = m
      outputs = (np.random.random(shape) - 0.5) * magnitude
      targets = np.random.random(shape)
      x = outputs - targets
      expected_result = np.zeros(shape)
      I1, J1 = np.where(abs(x) < 1.0 / sigma2)
      I2, J2 = np.where(abs(x) >= 1.0 / sigma2)
      expected_result[I1, J1] = x[I1, J1] * sigma2
      expected_result[I2, J2] = np.sign(x[I2, J2])
      compare_tensors(
          SmoothL1Loss(sigma=sigma),
          outputs,
          targets,
          expected_result,
          deriv=True,
          tol=1e-5,
      )
  
imports: import itertools
  import numpy
  from neon import NervanaObject
  from neon.backends import gen_backend
  from neon.transforms import CrossEntropyBinary, CrossEntropyMulti, SumSquared, MeanSquared, Misclassification, PrecisionRecall, SmoothL1Loss, SquareHingeLoss
label_desc: Use image node_id 22 for calling a global function with example usage: test_smoothL1_random_derivative(backend_default, fargs) without return types